Wireless sensor networks (WSNs) are gaining worldwide popularity due to their broad applications in different environments, including office, home, and hostile areas. Such WSNs may present a meaningful and efficient solution to some challenging problems, such as building safety monitoring, vehicle tracking, wildlife tracking, and environmental surveillance. Advances in micro electromechanical system technology (MEMS), combined with radio frequency (RF) circuits and low cost, low power digital signal processors (DSPs), improve feasibility of these sensor networks.
A WSN may consist of multiple sensor nodes that sense data of interest and transmit the sensed data, directly or indirectly, to a remote database for further processing. For example, FIG. 1 shows a Wireless Integrated Network Sensor Next Generation (WINS NG) network 100 corresponding to FIG. 8 of U.S. Pat. No. 7,020,701. Referring to FIG. 1, network 100 includes nodes 102, gateway nodes 104, a server 106, and web assistants or node control web or browser pages (not shown). In the network 100, the sensor nodes 102 are constructed in a layered fashion to enable use of standard tools, facilitate real-time operating systems issues, promote adaptability to unknown environments, simplify reconfiguration, and enable lower-power, continuously vigilant operation.
Sensor nodes are usually power constrained and have limited computational and communication power in a WSN. Therefore it may be desirable to maximize lifetime of the sensor nodes under this constraint. The lifetime of the sensor nodes depends on effective energy saving strategies such as sensor scheduling and in-network information processing to reduce the amount of sensed data transmitted to a remote database.
One exemplary in-network information processing technique is data aggregation, which has been utilized as a paradigm for wireless routing in sensor networks. Since sensor nodes are usually energy constrained, it may be inefficient and power consuming for all of the sensor nodes to transmit sensed data directly to a remote database for processing. Data sensed by neighboring sensor nodes is often highly correlated and hence redundant. In addition, the amount of the sensed data in a WSN of large size is usually very large for a remote database to process. Data aggregation is a technique that can aggregate data at neighboring sensor nodes or intermediate nodes, which may reduce the amount of the sensed data transmitted to the remote database. As a result, data aggregation can save energy and improve bandwidth utilization for WSNs.
Two commonly used sensor network architectures are self-organized WSNs and clustered WSNs. FIG. 2 illustrates a conventional self-organized WSN 200. With reference to FIG. 2, each sensor node 202-1, 202-2, . . . , 202-M (M is the total number of sensor nodes in the WSN 200) senses certain parameters, such as temperature, pressure, or humidity, of an environment, and transmits data to a remote database 204 by radio communication. The data may be transmitted to the remote database 204 directly or indirectly.
Data aggregation in the WSN 200 may be performed at different sensor nodes along a multi-hop path (e.g., the sensor node 202-3→the sensor node 202-2→the sensor node 202-1). By aggregating data at the different sensor nodes in the multi-hop path, data aggregation can help eliminate data redundancy and minimize data transmissions to the remote database 204. However, high latency may be involved in data transmission to the remote database 204 via the multi-hop path. In addition, although the self-organized WSN 200 is easy to construct, the sensor nodes 202-1, 202-2, . . . , 202-M may be highly power consuming in data transmission, which may result in a short operation lifetime for the WSN 200.
As mentioned above, it may be inefficient for all of the sensors to transmit sensed data directly to the remote database for processing, especially in a WSN of large size. To save energy and improve bandwidth utilization, the WSN can be divided into non-overlapping clusters, wherein a cluster includes a group of sensor nodes and a local aggregator or a cluster head which aggregates data from all of the sensor nodes in its own cluster and transmits the aggregated data to the remote database. By aggregating data coming from different sensor nodes in the same cluster, data aggregation can help eliminate data redundancy and minimize data transmissions to the remote database. As a result, dividing the WSN into clusters and aggregating data can save energy and improve bandwidth utilization for the WSN.
FIG. 3 illustrates a conventional clustered WSN 300. With reference to FIG. 3, the WSN 300 is divided into non-overlapping clusters 302-1, 302-2, . . . , 302-N (N is the total number of clusters in the WSN 300) with a powerful node, an aggregator or a cluster head, 304-1, 304-2, . . . , 304-N in each cluster. Each sensor node 306-1, 306-2, . . . , 306-M (M is the total number of sensor nodes in the WSN 300) senses certain parameters, such as temperature, pressure, or humidity, of an environment, and transmits data to the one of the aggregators 304-1, 304-2, . . . , 304-N in its own cluster. Each aggregator 304-1, 304-2, . . . , 304-N then aggregates the data from the different sensor nodes in its own cluster (e.g., the aggregator 304-1 aggregates the data from the sensor nodes 306-1, 306-2, and 306-3 in the cluster 302-1) and wirelessly transmits the aggregated data to a remote database 308 for further processing. Because the aggregators 304-1, 304-2, . . . , 304-N can eliminate data redundancy and minimize data transmissions to the remote database 308, the clustered WSN 300 may have a longer operation lifetime compared to the self-organized WSN 200 in FIG. 2.
While data aggregation can help conserve energy resources by reducing data redundancy and improve bandwidth utilization, security issues may exist in WSNs. Such security issues include data secrecy and data privacy. In terms of data secrecy, sensed data should be protected from attacks, such as known-ciphertext attacks, known-plaintext attacks, and relay attacks, during data transmission. In terms of privacy, the sensed data should remain secret to aggregators. For example, each aggregator 304-1, 304-2, . . . , 304-N should not know contents of the sensed data received from any of the sensor nodes 306-1, 306-2, . . . , 306-M in its own cluster.